Marketing Analytics with R
About the training
Why are we losing customers? Is this campaign even working? What type of people are my best customers?
Those questions are common for nearly all businesses regardless of how successful they are. Every company wants to grow and expand their customer base. Marketing analytics allows you to better understand customer behavior and recognize the needs of potential consumers. This training is an introduction to the analytical tools used in direct marketing. We will show you how to solve common problems such as choosing recipients of a marketing campaign, customer segmentation, and modeling migration.
Examples and exercises are done in R, a popular tool for marketing analytics. R is a free, open source language that is constantly updated and expanded to include latest statistical methods. R can process large amounts of data and is also a great tool for data visualization. Using R you will be able to write your own procedures, enhance and automate your daily work.
Who is this training for?
If you encounter in your work marketing problems such as customer retention, measuring the effectiveness of a campaign, identifying market trends or segmenting customer base this training is right for you. Using quantitative analysis you will be able to understand your customers better, improve your marketing and grow your business.
What will I learn?
After completing the training, participants will be able to:
- Visualize large datasets using meaningful and beautiful charts that are easy to interpret
- Choose the right statistical model for your problem
- Build models to solve common direct marketing problems such as customer segmentation
- Write advanced R scripts to automate data analytics process
- What is Marketing and Marketing Analytics?
- IBRO model
- Why R?
- New customers acquisition – marketing campaign analysis
- Measure campaign effectiveness – Test group and control group
- Generalized linear model (GLM) and logistic regression
- Growth curve
- Uplift modelling
- Introduction to decision trees
- Campaign profitability
- Managing existing customers – customer segmentation
- Types of segmentation
- Segmentation using decision trees
- RFM models
- Customer retention – customer migration analysis
- Types of customer churn
- ROC and AUC curves
- Modeling customer migration: glm, ctree, rpart, gbm, randomForest
- Optimizing model using caret package
- Customer churn – churn survey
- Measuring customer attrition
- Measuring customer value
- Cluster analysis: kmeans, clara, flexclust